Study On Improved Two-phase Fisher Discriminant Analysis Algorithm And Its Application To Face Recognition

Along with the rapid development of computer technology and image processingtechnology, face recognition technology has become a concerned research focus in areassuch as world peace, state security and social stability, and has gradually formed animportant technology of biological characteristics identification. Especially since the1990s, the further research of principal component analysis (PCA) and linear discriminantanalysis (LDA), which are the representative of subspace method, has effectivelypromoted the development of face recognition technology. As a result, face recognitiontechnology is widely used in areas such as prevention of terrorism, anti-terrorism, criminalinvestigation, code management, and is becoming an indispensable important means and abenchmark method in criminal investigation field. This paper focuses on finding therelevant points between the two kinds of subspace methods through the study on theexisting algorithms and key problems of them. It is designed to extract mostdiscriminating facial features without loss of expressive power, and to put forward somenew effective algorithms. The main works of this article are as follows:The research spreads around the application of the mirror symmetry characteristic offacial image sample in principal component analysis. Face presents a relatively strictcharacteristic of mirror symmetry about front center vertical line. The use of mirrorimages as a class of virtual face samples can expand the size of training sample set andovercome the impact of factors such as facial pose, visual angle, and rotation changes. Onthe basis of deep analysis of existing symmetrical PCA algorithms, existing problems ofkernel based symmetrical PCA (KSPCA) algorithm are studied and improved, such as thelimitation of selection type of kernel function, the lack of concrete analysis ability aimingat specific samples, and so on. And a generalized kernel based symmetrical PCA(GKSPCA) algorithm is proposed, which is based on the interpolation kernel method.Some key problems of Fisher discriminant analysis are studied, including thepromotion of discriminant criterion, and orthogonal and statistically uncorrelated set ofdiscriminant vectors. In view of problems of the existing two-phase algorithms, includingthe limitation of types and numbers of discriminant vector and dimensions of discriminant feature, and statistical correlation of discriminant feature, two new two-phase kernel basedFisher discriminant analysis algorithms are proposed based on the algorithm ideaabout"extraction of discriminant features in the expressive space", includingWKPCA+LDA and GKSPCA+SLDA. Among them, WKPCA namely is whitened kernelbased PCA, and SLDA namely is LDA based on Fisher symmetrical null spacediscriminant criterion. In WKPCA+LDA algorithm, the non-linear uncorrelated spacebased optimal discriminant vectors (NLUSODVs) and its generalized solution method areput forward. The WKPCA+LDA algorithm can realize that the extremal problem of Fisheroptimal discriminant vectors solved in the non-linear statistically uncorrelated constraintspace (WKPCA subspace). Under the premise without loss of generality, theWKPCA+LDA algorithm can extract more discriminating features as many as possible,which are more conducive to face recognition. In GKSPCA+SLDA algorithm, thetwo-stage symmetrical criterion is promoted and applied, which is aimed to increase thetype and number of discriminant vectors and the dimension of discriminant features, thusto achieve the maximization of expressive and discriminating power of face recognitionfeatures. In face recognition experiments, the influences of solving methods and selectionstrategies of discriminant vectors on recognition performance are analyzed, and comparedwith traditional algorithms, advantages of the two new algorithms are raised.New algorithms of image enhancement are put forward, which combined with thenew Fisher discriminant analysis algorithms will make face recognition much moreaccurate and quicker.Various two-directional two-dimensional linear algorithms of Fisher discriminantanalysis are discussed through face recognition experiments, including two-directionaltwo-phase algorithms and two-directional combined algorithms. The influences of solvingmethods of two-directional projection matrixs and feature selection strategies onrecognition performance are analyzed. At the same time, the regularities are verified byexperiments.